From: Matt Mower <matt.mower@...>
Date: 2005-04-15T16:56:45+09:00
Subject: [ANN] Bishop 0.3.0 - bayesian classifier for Ruby ported from Python

Hi folks,

I've recently released a Ruby port "Bishop" of the "Reverend" bayesian
classifier written in Python. Bishop-0.3.0 is available as a Gem and
from RubyForge

http://rubyforge.org/projects/bishop/

Bishop is a reasonably direct port of the original Python code, bug
reports and suggestions for improving the structure of the code would
be welcomed.

Bishop includes both Robinson and Robinson-Fisher algorithms for
classification.  It is presumed that they were correctly implemented
in Reverend.  I aim to test this in my own use of the code.

Support is included for saving/loading the trained classifier to/from YAML.

An example of using Bishop:

  require 'bishop'

  b = Bishop::Bayes.new
  b.train( "ham", "a great message from a close friend" )
  b.train( "spam", "buy viagra here" )
  puts b.guess( "would a friend send you a viagra advert?" )

  => [ [ "ham", <prob> ], [ "spam", <prob> ] ]

Bishop defaults to using the Robinson algorithm.  To use a different
algorithm construct the classifier passing a block which will call the
choosen algorithm:

  Bishop::Bayes.new { |probs,ignore| Bishop::robinson_fisher( probs, ignore ) }

To save to a YAML file:

  b.save "myclassifier.yaml"

To load from a YAML file:

  b.load "myclassifier.yaml"

You can uniquely identify training items

  b.train( "ham", "friends don't let friends develop on shared
hosting", "<xyz.323-ON8.002-802.002@ham.com>" )

An can untrain items:

  b.untrain( <pool>, <item>[, <uid> ] )

I'm using this in a project of my own and would welcome any feedback
or suggested improvements.

Regards,

Matt

-- 
Matt Mower :: http://matt.blogs.it/